With continuing improvements in medical imaging technology and computer processing systems, computer assisted analysis of medical imaging data is a growing technology area. In particular, analysis of medical images of a brain scan may assist in identifying and/or assessing neurological injuries and/or conditions. For example, a number of studies have indicated that structural differences may be found in brains of individuals of subjects exhibiting dyslexia, autism, and attention deficit/hyperactivity disorder (ADHD).
The neurological disorder of dyslexia, for example, is difficult to diagnose and has a profound impact on a child's ability to fluently read and comprehend words despite the fact that they possess a normal intelligence level for their age and education [1]. Dyslexia is not uncommon, as it affects roughly 5-12% of the population [2]. However, it is often diagnosed only after a child's scholastic performance or lifestyle has already been impacted.
According to multiple studies, structural differences are found in the brains of subjects with dyslexia. The earliest finding revealed a lack of the typical brain asymmetry and an increase in cortical anomalies [2]. According to Eliez et al. [3] and Casanova et al. [4], dyslexic subjects have smaller gyral indexes (the ratios between the pial contours and the convex hull of the brain surface) than normal subjects, suggesting that the dyslexic brains differ in folding. The recent comprehensive reviews by Richlan et al. [5] and Krafnick et al. [6] have demonstrated evidence of change in the bilateral temporoparietal and left occipitotemporal cortical regions of the brain's gray matter.
Using voxel-based morphometry to examine in-vivo dyslexic brains, Eliez et al. [3] and Silani et al. [7] have indicated reduced gray matter volume in such brains. Klingberg et al. [8] and Niogi et al. [9] also examined the cerebral white matter by using diffusion tensor imaging and found similar results. By analyzing MRIs, Elnakib et al. [10] and von Plessen et al. [11] discovered significant differences in the shapes and body length of the corpus callosum in key anatomical regions that help to identify dyslexia.
Advances in neuro-imaging provide some possibilities for non-invasive methods for automatic dyslexia and/or autism detection by revealing differences between quantitative characteristics of normal, autistic, and dyslexic brains. Studies have shown a relation between the cerebral white matter (CWM) volume and anatomy in dyslexic and autistic brains as compared with normal brains. Particularly, the CWM structural differences may generally be related to the volume of the CWM, where an autisic brain is reported as having a larger volume than a normal brain and a dyslexic brain is reported as having a smaller volume than a normal brain.
Some conventional computer aided diagnostic systems utilize a volumetric analysis to classify a brain as autistic or dyslexic. However, a volumetric approach fails to account for other factors including, for example, age, and gender. Thus, while volume has been linked to autism and dyslexia, computer aided analysis utilizing volume as a discriminating factor is not particularly accurate because of brain volume differences due to age and gender.
Therefore, a need continues to exist in the art for improved image processing techniques for use in analyzing medical images of the brain for the purposes of assessing neurological conditions of the brain.